Pollution-based integrated real-time control for urban drainage systems: a multi-agent deep reinforcement learning approach

Abstract

This study presents a multi-agent reinforcement learning (MARL) framework for integrated real-time control (RTC) of urban drainage systems (UDSs), coordinating sewers, wastewater treatment plants (WWTPs), and receiving waters. Trained within a hydraulic–water quality simulation environment using QMIX, the framework enables facility-level decision-making and adaptive system coordination. Applied to Lu’an City, China, MARL achieved a 25.4% reduction in flooding and overflow volumes and an 18.0% decrease in river pollutants relative to benchmark strategies, while maintaining real-time control feasibility (6.35 s per 5-min interval). Under rainfall forecast and sensor noise uncertainty, MARL improved performance stability by 44.7–52.4%. Despite operational trade-offs, the framework supports integrated system optimization and consistent water quality improvements in urban settings.